OpenRaiser/Intern-Atlas huggingface dataset:
Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernels
Venue: arXiv cs.LG 2021
Year: 2021

As a tool for capturing irregular temporal dependencies (rather than resorting to binning temporal observations to construct time series), Hawkes processes with exponential decay have seen widespread adoption across many application domains, such as predicting the occurrence time of the next earthquake or stock market spike. However, practical applications of Hawkes processes face a noteworthy challenge: There is substantial and often unquantified variance in decay parameter estimations, especially in the case of a small number of observations or when the dynamics behind the observed data suddenly change. We empirically study the cause of these practical challenges and we develop an approach to surface and thereby mitigate them. In particular, our inspections of the Hawkes process likelihood function uncover the properties of the uncertainty when fitting the decay parameter. We thus propose to explicitly capture this uncertainty within a Bayesian framework. With a series of experiments with synthetic and real-world data from domains such as "classical" earthquake modeling or the manifestation of collective emotions on Twitter, we demonstrate that our proposed approach helps to quantify uncertainty and thereby to understand and fit Hawkes processes in practice.
Distributional Offline Continuous-Time Reinforcement Learning with Neural Physics-Informed PDEs (SciPhy RL for DOCTR-L)
Venue: arXiv cs.LG 2021
Year: 2021

This paper addresses distributional offline continuous-time reinforcement learning (DOCTR-L) with stochastic policies for high-dimensional optimal control. A soft distributional version of the classical Hamilton-Jacobi-Bellman (HJB) equation is given by a semilinear partial differential equation (PDE). This `soft HJB equation' can be learned from offline data without assuming that the latter correspond to a previous optimal or near-optimal policy. A data-driven solution of the soft HJB equation uses methods of Neural PDEs and Physics-Informed Neural Networks developed in the field of Scientific Machine Learning (SciML). The suggested approach, dubbed `SciPhy RL', thus reduces DOCTR-L to solving neural PDEs from data. Our algorithm called Deep DOCTR-L converts offline high-dimensional data into an optimal policy in one step by reducing it to supervised learning, instead of relying on value iteration or policy iteration methods. The method enables a computable approach to the quality control of obtained policies in terms of both their expected returns and uncertainties about their values.
Fairness and Communication-Based Semantics for Session-Typed Languages
Venue: arXiv cs.PL 2021
Year: 2021

We give communication-based semantics and reasoning techniques for Polarized SILL, a rich session-typed programming language with general recursion. Its features include channel and code transmission, synchronous and asynchronous communication, and functional programming. Our contributions are distinguished by their faithfulness to the process abstraction, i.e., to the premise that communication is the only observable phenomenon of processes. We give the first observed communication semantics that supports general recursion and code transmission. Observed communication semantics define the meaning of processes in terms of their observed communications. We use this observational semantics to define experiments on processes, and we give a communication-based testing equivalences framework for defining observational simulations and equivalences on processes. This framework captures several natural equivalences, and we show that one of these coincides with barbed congruence, the canonical notion of process equivalence. Polarized SILL is defined using a substructural operational semantics based on multiset rewriting. To ensure that our contributions are well-defined in the presence of non-termination, we introduce fairness for multiset rewriting systems. We construct a fair scheduler, we give sufficient conditions for traces to be fair, and we study the effects of permutation on fair traces.
Adaptive Bitrate Streaming Over Cellular Networks: Rate Adaptation and Data Savings Strategies
Venue: arXiv cs.NI 2021
Year: 2021

Adaptive bitrate streaming (ABR) has become thede factotechnique for videostreaming over the Internet. Despite a flurry of techniques, achieving high quality ABRstreaming over cellular networks remains a tremendous challenge. First, the design ofan ABR scheme needs to balance conflicting Quality of Experience (QoE) metrics suchas video quality, quality changes, stalls and startup performance, which is even harderunder highly dynamic bandwidth in cellular network. Second, streaming providers havebeen moving towards using Variable Bitrate (VBR) encodings for the video content,which introduces new challenges for ABR streaming, whose nature and implicationsare little understood. Third, mobile video streaming consumes a lot of data. Althoughmany video and network providers currently offer data saving options, the existingpractices are suboptimal in QoE and resource usage. Last, when the audio and videotracks are stored separately, video and audio rate adaptation needs to be dynamicallycoordinated to achieve good overall streaming experience, which presents interestingchallenges while, somewhat surprisingly, has received little attention by the researchcommunity. In this dissertation, we tackle each of the above four challenges.
Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions
Venue: arXiv cs.CV 2021
Year: 2021

Purpose: This article develops theoretical, algorithmic, perceptual, and interaction aspects of script legibility enhancement in the visible light spectrum for the purpose of scholarly editing of papyri texts. - Methods: Novel legibility enhancement algorithms based on color processing and visual illusions are compared to classic methods in a user experience experiment. - Results: (1) The proposed methods outperformed the comparison methods. (2) Users exhibited a broad behavioral spectrum, under the influence of factors such as personality and social conditioning, tasks and application domains, expertise level and image quality, and affordances of software, hardware, and interfaces. No single enhancement method satisfied all factor configurations. Therefore, it is suggested to offer users a broad choice of methods to facilitate personalization, contextualization, and complementarity. (3) A distinction is made between casual and critical vision on the basis of signal ambiguity and error consequences. The criteria of a paradigm for enhancing images for critical applications comprise: interpreting images skeptically; approaching enhancement as a system problem; considering all image structures as potential information; and making uncertainty and alternative interpretations explicit, both visually and numerically.
NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition
Venue: arXiv cs.CV 2021
Year: 2021

In the field of complex action recognition in videos, the quality of the designed model plays a crucial role in the final performance. However, artificially designed network structures often rely heavily on the researchers' knowledge and experience. Accordingly, because of the automated design of its network structure, Neural architecture search (NAS) has achieved great success in the image processing field and attracted substantial research attention in recent years. Although some NAS methods have reduced the number of GPU search days required to single digits in the image field, directly using 3D convolution to extend NAS to the video field is still likely to produce a surge in computing volume. To address this challenge, we propose a new processing framework called Neural Architecture Search- Temporal Convolutional (NAS-TC). Our proposed framework is divided into two phases. In the first phase, the classical CNN network is used as the backbone network to complete the computationally intensive feature extraction task. In the second stage, a simple stitching search to the cell is used to complete the relatively lightweight long-range temporal-dependent information extraction. This ensures our method will have more reasonable parameter assignments and can handle minute-level videos. Finally, we conduct sufficient experiments on multiple benchmark datasets and obtain competitive recognition accuracy.
Fast Parallel Algorithms for Euclidean Minimum Spanning Tree and Hierarchical Spatial Clustering
Venue: arXiv cs.DS 2021
Year: 2021

This paper presents new parallel algorithms for generating Euclidean minimum spanning trees and spatial clustering hierarchies (known as HDBSCAN$^*$). Our approach is based on generating a well-separated pair decomposition followed by using Kruskal's minimum spanning tree algorithm and bichromatic closest pair computations. We introduce a new notion of well-separation to reduce the work and space of our algorithm for HDBSCAN$^*$. We also present a parallel approximate algorithm for OPTICS based on a recent sequential algorithm by Gan and Tao. Finally, we give a new parallel divide-and-conquer algorithm for computing the dendrogram and reachability plots, which are used in visualizing clusters of different scale that arise for both EMST and HDBSCAN$^*$. We show that our algorithms are theoretically efficient: they have work (number of operations) matching their sequential counterparts, and polylogarithmic depth (parallel time). We implement our algorithms and propose a memory optimization that requires only a subset of well-separated pairs to be computed and materialized, leading to savings in both space (up to 10x) and time (up to 8x). Our experiments on large real-world and synthetic data sets using a 48-core machine show that our fastest algorithms outperform the best serial algorithms for the problems by 11.13--55.89x, and existing parallel algorithms by at least an order of magnitude.
Detecting Anomalies Through Contrast in Heterogeneous Data
Venue: arXiv cs.LG 2021
Year: 2021

Detecting anomalies has been a fundamental approach in detecting potentially fraudulent activities. Tasked with detection of illegal timber trade that threatens ecosystems and economies and association with other illegal activities, we formulate our problem as one of anomaly detection. Among other challenges annotations are unavailable for our large-scale trade data with heterogeneous features (categorical and continuous), that can assist in building automated systems to detect fraudulent transactions. Modelling the task as unsupervised anomaly detection, we propose a novel model Contrastive Learning based Heterogeneous Anomaly Detector to address shortcomings of prior models. Our model uses an asymmetric autoencoder that can effectively handle large arity categorical variables, but avoids assumptions about structure of data in low-dimensional latent space and is robust to changes to hyper-parameters. The likelihood of data is approximated through an estimator network, which is jointly trained with the autoencoder,using negative sampling. Further the details and intuition for an effective negative sample generation approach for heterogeneous data are outlined. We provide a qualitative study to showcase the effectiveness of our model in detecting anomalies in timber trade.
Trends in eBusiness and eGovernment
Venue: arXiv cs.CY 2021
Year: 2021

The first chapter is a critical review and a case study in eBusiness, with special attention to the digital currencies resource and its possibilities. 2. chapter attempts to incorporate the UTAUT model with perceived risk theory to explore its impact on the intention to use m-government services. 3. chapter aims to assess the level of gender inclusivity in the municipal e-procurement processes in the City of Johannesburg as a case study. It uses a GAD approach. 4. chapter examines the impediments that derail the intensive uptake of eLearning programmes in a particular higher education institution. The study adopted an inductive research paradigm that followed a qualitative research strategy. Data were collected by means of one-on-one in-depth interviews from selected faculty members at a nominated institution of higher learning. 5. chapter investigated the role of KMS in enhancing the export performance of firms operating within the manufacturing sector in Zimbabwe. The study used a quantitative approach in which a survey questionnaire was distributed to 555 managers drawn from 185 manufacturing firms based in Harare. Data analyses involved the use of descriptive statistics, Spearman correlations and regression analysis. In the sixth chapter, a survey was undertaken on 131 SMEs from the Pelagonija region in order to determine the current level of SME digitalization within the region. It is aimed to compare with the EU average and to make conclusions on the impact of the SME digitalization on region GDP growth as well as revenues collection. The last chapter s purpose was to develop a measuring and modelling framework, an instrument of IBSQ for the South African banking sector. Snowball and convenience sampling, both non-probability techniques were used to recruit participants for the study. A total of 310 Internet banking customer responses were utilised in the analysis.
grASP: A Graph Based ASP-Solver and Justification System
Venue: arXiv cs.AI 2021
Year: 2021

Answer set programming (ASP) is a popular nonmonotonic-logic based paradigm for knowledge representation and solving combinatorial problems. Computing the answer set of an ASP program is NP-hard in general, and researchers have been investing significant effort to speed it up. The majority of current ASP solvers employ SAT solver-like technology to find these answer sets. As a result, justification for why a literal is in the answer set is hard to produce. There are dependency graph based approaches to find answer sets, but due to the representational limitations of dependency graphs, such approaches are limited. We propose a novel dependency graph-based approach for finding answer sets in which conjunction of goals is explicitly represented as a node which allows arbitrary answer set programs to be uniformly represented. Our representation preserves causal relationships allowing for justification for each literal in the answer set to be elegantly found. Performance results from an implementation are also reported. Our work paves the way for computing answer sets without grounding a program.
FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface
Venue: arXiv cs.OH 2021
Year: 2021

Lack of adequate training samples and noisy high-dimensional features are key challenges faced by Motor Imagery (MI) decoding algorithms for electroencephalogram (EEG) based Brain-Computer Interface (BCI). To address these challenges, inspired from neuro-physiological signatures of MI, this paper proposes a novel Filter-Bank Convolutional Network (FBCNet) for MI classification. FBCNet employs a multi-view data representation followed by spatial filtering to extract spectro-spatially discriminative features. This multistage approach enables efficient training of the network even when limited training data is available. More significantly, in FBCNet, we propose a novel Variance layer that effectively aggregates the EEG time-domain information. With this design, we compare FBCNet with state-of-the-art (SOTA) BCI algorithm on four MI datasets: The BCI competition IV dataset 2a (BCIC-IV-2a), the OpenBMI dataset, and two large datasets from chronic stroke patients. The results show that, by achieving 76.20% 4-class classification accuracy, FBCNet sets a new SOTA for BCIC-IV-2a dataset. On the other three datasets, FBCNet yields up to 8% higher binary classification accuracies. Additionally, using explainable AI techniques we present one of the first reports about the differences in discriminative EEG features between healthy subjects and stroke patients. Also, the FBCNet source code is available at https://github.com/ravikiran-mane/FBCNet.
ICFP 2020 Post-Conference Report
Venue: arXiv cs.DL 2021
Year: 2021

This document describes the ICFP 2020 virtual conference, including the planning process and the criteria that informed its design, plus feedback from the post-conference survey. It is intended to provide a record of the event and give advice to future organizers of virtual conferences.
Small World Student Network at the University of Texas at Dallas in Times of Social Distancing
Venue: arXiv cs.SI 2021
Year: 2021

To limit the spread of the novel coronavirus on college campuses, a common strategy for the Fall 2020 and Spring 2021 terms has been to offer instruction weighted toward hybrid or fully online modalities. Colleges are now considering whether and how to expand hybrid or fully in-person instruction for future terms, and learn lessons from this experience for future use. Our paper uses Fall 2019 enrollment data for a medium-sized public American university to analyze whether some student groupings by class standing or course level are more susceptible to the spread of infectious disease through academic enrollment networks. Replicating Weeden and Cornwell [8], we find that enrollment networks at the institution are "small worlds" characterized by high clustering, short average path lengths, and multiple independent connections. Connectivity decreases as class standing (graduate vs. undergraduate; senior vs. freshman) and course level increase; as students move from generalized to specialized course loads, networks cluster by major. Holding other factors constant, policies focusing on in-person instruction for lower division students conflict with the greater risk of infectious spread through a lower division network in the absence of additional steps to minimize academic connectivity. There are academic and financial incentives for emphasizing the freshman experience, including concerns about student attrition from the first to second academic year and recouping costs of infrastructure investments in dormitories. Possible solutions could include (i) restricting face-to-face or hybrid instruction to courses in students' academic majors, which would disrupt larger networks into smaller ones and thus restrict the spread of infection across majors, and (ii) take a "scalpel" approach to instruction modes by moving online courses most likely to facilitate epidemic spread.
Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems
Venue: arXiv cs.CL 2021
Year: 2021

Intent Recognition and Slot Identification are crucial components in spoken language understanding (SLU) systems. In this paper, we present a novel approach towards both these tasks in the context of low resourced and unwritten languages. We present an acoustic based SLU system that converts speech to its phonetic transcription using a universal phone recognition system. We build a word-free natural language understanding module that does intent recognition and slot identification from these phonetic transcription. Our proposed SLU system performs competitively for resource rich scenarios and significantly outperforms existing approaches as the amount of available data reduces. We observe more than 10% improvement for intent classification in Tamil and more than 5% improvement for intent classification in Sinhala. We also present a novel approach towards unsupervised slot identification using normalized attention scores. This approach can be used for unsupervised slot labelling, data augmentation and to generate data for a new slot in a one-shot way with only one speech recording
Learning the CSI Recovery in FDD Systems
Venue: arXiv cs.IT 2021
Year: 2021

We propose an innovative machine learning-based technique to address the problem of channel acquisition at the base station in frequency division duplex systems. In this context, the base station reconstructs the full channel state information in the downlink frequency range based on limited downlink channel state information feedback from the mobile terminal. The channel state information recovery is based on a convolutional neural network which is trained exclusively on collected channel state samples acquired in the uplink frequency domain. No acquisition of training samples in the downlink frequency range is required at all. Finally, after a detailed presentation and analysis of the proposed technique and its performance, the "transfer learning'' assumption of the convolutional neural network that is central to the proposed approach is validated with an analysis based on the maximum mean discrepancy metric.
Uncertainty for Identifying Open-Set Errors in Visual Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

Deployed into an open world, object detectors are prone to open-set errors, false positive detections of object classes not present in the training dataset. We propose GMM-Det, a real-time method for extracting epistemic uncertainty from object detectors to identify and reject open-set errors. GMM-Det trains the detector to produce a structured logit space that is modelled with class-specific Gaussian Mixture Models. At test time, open-set errors are identified by their low log-probability under all Gaussian Mixture Models. We test two common detector architectures, Faster R-CNN and RetinaNet, across three varied datasets spanning robotics and computer vision. Our results show that GMM-Det consistently outperforms existing uncertainty techniques for identifying and rejecting open-set detections, especially at the low-error-rate operating point required for safety-critical applications. GMM-Det maintains object detection performance, and introduces only minimal computational overhead. We also introduce a methodology for converting existing object detection datasets into specific open-set datasets to evaluate open-set performance in object detection.
Sparse Universum Quadratic Surface Support Vector Machine Models for Binary Classification
Venue: arXiv cs.LG 2021
Year: 2021

In binary classification, kernel-free linear or quadratic support vector machines are proposed to avoid dealing with difficulties such as finding appropriate kernel functions or tuning their hyper-parameters. Furthermore, Universum data points, which do not belong to any class, can be exploited to embed prior knowledge into the corresponding models so that the generalization performance is improved. In this paper, we design novel kernel-free Universum quadratic surface support vector machine models. Further, we propose the L1 norm regularized version that is beneficial for detecting potential sparsity patterns in the Hessian of the quadratic surface and reducing to the standard linear models if the data points are (almost) linearly separable. The proposed models are convex such that standard numerical solvers can be utilized for solving them. Nonetheless, we formulate a least squares version of the L1 norm regularized model and next, design an effective tailored algorithm that only requires solving one linear system. Several theoretical properties of these models are then reported/proved as well. We finally conduct numerical experiments on both artificial and public benchmark data sets to demonstrate the feasibility and effectiveness of the proposed models.